Python is quickly becoming the go-to language for data analysis. However, it can be difficult to figure out which tools are good to use. In this workshop, we’ll work through in-depth examples of tools for data wrangling, machine learning, and data visualization. I’ll show you how to work through a data analysis workflow, and how to deal with different kinds of data.
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Metaprograming in Python is fun and profitable thanks to its rich Data Model – APIs that let you handle functions, modules and even classes as objects that you can create, inspect and modify at runtime. The Data Model also enables your own objects to support infix operators, become iterable and emulate collections. This workshop shows how, through a diverse selection of examples and exercises.
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Learn Test-Driven-Development and how it applies to web applications by building a simple web app from scratch using Python and Django. We'll cover unit testing, Django models, views and templates, as well as using Selenium to open up a real web browser for functional tests.
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The Consumer Financial Protection Bureau (http://cfpb.gov) has
developed an open source web-based tool to make regulations easy to
read, access and understand. We talk about the unique parsing and
other challenges we encountered working with these legal documents,
and how we used Python, pyParsing, Django and other open source tools
to solve them.
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As technologists, sometimes it’s as important to be able to share information with others as to be able to actually build something. IPython notebook is a powerful tool to both experiment with code (and data) and share the results with others, technical and non-technical alike. This session introduces the notebook and gives examples and techniques for using it effectively.
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If you're pushing the envelope of programming (or of your own skills)... and even when you’re not... there *will* be bugs in your code. Don't panic! We cover the attitudes and skills (not taught in most schools) to minimize your bugs, track them, find them, fix them, ensure they never recur, and deploy fixes to your users.
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Python is quickly becoming the go-to language for data analysis, but it can be difficult to figure out which tools to use. In this presentation, I’ll give a bird’s eye overview of some of the best tools for data analysis and how you can apply them to your own workflow. I’ll introduce you to how you can use Pandas, Scikit-Learn, NLTK, MRJob, and matplotlib for data analysis.
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Asynchronous frameworks like Tornado, Twisted, and Node are increasingly important for writing high-performance web applications. Even if you’re an experienced web programmer, you may lack a rigorous understanding of how these frameworks work and when to use them. See how Tornado's event loop works, and learn how to efficiently handle very large numbers of concurrent connections.
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Python has no private fields, but the property decorator lets you replace public attributes with getters and setters without breaking client code. And the descriptor mechanism, used in Django for model field declarations, enables wide reuse of getter/setter logic via composition instead of inheritance. This talk explains how properties and descriptors work by refactoring a practical example.
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Python has a complex past with crypto. There are half a dozen frameworks built on at least three separate C implementations, each with their own strengths and weaknesses and in various states of maintenance. This presentation will review the current state of the art and discuss the future of crypto in Python including a new library aimed at fixing modern crypto support in Python.
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The key to writing Pythonic classes, APIs and frameworks is leveraging the Python Data Model: a set of interfaces which, when implemented in your classes, enables them to leverage fundamental language features such as iteration, context managers, infix operators, attribute access control etc. This talk shows how, through a diverse selection of examples.
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